Overview

Dataset statistics

Number of variables27
Number of observations205
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.4 KiB
Average record size in memory216.6 B

Variable types

Numeric16
Categorical11

Alerts

modèle has a high cardinality: 141 distinct valuesHigh cardinality
identifiant is highly overall correlated with marqueHigh correlation
etat_de_route is highly overall correlated with empattement and 2 other fieldsHigh correlation
empattement is highly overall correlated with etat_de_route and 11 other fieldsHigh correlation
longueur is highly overall correlated with empattement and 9 other fieldsHigh correlation
largeur is highly overall correlated with empattement and 10 other fieldsHigh correlation
hauteur is highly overall correlated with etat_de_route and 3 other fieldsHigh correlation
poids_vehicule is highly overall correlated with empattement and 8 other fieldsHigh correlation
taille_moteur is highly overall correlated with empattement and 12 other fieldsHigh correlation
taux_alésage is highly overall correlated with empattement and 9 other fieldsHigh correlation
course is highly overall correlated with emplacement_moteur and 1 other fieldsHigh correlation
taux_compression is highly overall correlated with carburant and 3 other fieldsHigh correlation
chevaux is highly overall correlated with empattement and 11 other fieldsHigh correlation
tour_moteur is highly overall correlated with carburantHigh correlation
consommation_ville is highly overall correlated with longueur and 7 other fieldsHigh correlation
consommation_autoroute is highly overall correlated with empattement and 9 other fieldsHigh correlation
prix is highly overall correlated with empattement and 8 other fieldsHigh correlation
carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
turbo is highly overall correlated with taux_compression and 1 other fieldsHigh correlation
nombre_portes is highly overall correlated with etat_de_route and 2 other fieldsHigh correlation
type_vehicule is highly overall correlated with nombre_portesHigh correlation
roues_motrices is highly overall correlated with marqueHigh correlation
emplacement_moteur is highly overall correlated with empattement and 4 other fieldsHigh correlation
type_moteur is highly overall correlated with taille_moteur and 3 other fieldsHigh correlation
nombre_cylindres is highly overall correlated with largeur and 6 other fieldsHigh correlation
systeme_carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
marque is highly overall correlated with identifiant and 9 other fieldsHigh correlation
carburant is highly imbalanced (53.9%)Imbalance
emplacement_moteur is highly imbalanced (89.0%)Imbalance
nombre_cylindres is highly imbalanced (57.6%)Imbalance
identifiant is uniformly distributedUniform
modèle is uniformly distributedUniform
identifiant has unique valuesUnique
etat_de_route has 67 (32.7%) zerosZeros

Reproduction

Analysis started2023-04-25 09:52:10.880043
Analysis finished2023-04-25 09:52:40.993119
Duration30.11 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

identifiant
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:41.075736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.2
Q152
median103
Q3154
95-th percentile194.8
Maximum205
Range204
Interquartile range (IQR)102

Descriptive statistics

Standard deviation59.322565
Coefficient of variation (CV)0.57594723
Kurtosis-1.2
Mean103
Median Absolute Deviation (MAD)51
Skewness0
Sum21115
Variance3519.1667
MonotonicityStrictly increasing
2023-04-25T11:52:41.216847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
142 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
Other values (195) 195
95.1%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%

etat_de_route
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2023-04-25T11:52:41.330302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2023-04-25T11:52:41.419396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%

carburant
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
essence
185 
diesel
20 

Length

Max length7
Median length7
Mean length6.902439
Min length6

Characters and Unicode

Total characters1415
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowessence
2nd rowessence
3rd rowessence
4th rowessence
5th rowessence

Common Values

ValueCountFrequency (%)
essence 185
90.2%
diesel 20
 
9.8%

Length

2023-04-25T11:52:41.522166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:41.634715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
essence 185
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
e 595
42.0%
s 390
27.6%
n 185
 
13.1%
c 185
 
13.1%
d 20
 
1.4%
i 20
 
1.4%
l 20
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1415
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 595
42.0%
s 390
27.6%
n 185
 
13.1%
c 185
 
13.1%
d 20
 
1.4%
i 20
 
1.4%
l 20
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1415
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 595
42.0%
s 390
27.6%
n 185
 
13.1%
c 185
 
13.1%
d 20
 
1.4%
i 20
 
1.4%
l 20
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 595
42.0%
s 390
27.6%
n 185
 
13.1%
c 185
 
13.1%
d 20
 
1.4%
i 20
 
1.4%
l 20
 
1.4%

turbo
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
atmosphérique
168 
turbo
37 

Length

Max length13
Median length13
Mean length11.556098
Min length5

Characters and Unicode

Total characters2369
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowatmosphérique
2nd rowatmosphérique
3rd rowatmosphérique
4th rowatmosphérique
5th rowatmosphérique

Common Values

ValueCountFrequency (%)
atmosphérique 168
82.0%
turbo 37
 
18.0%

Length

2023-04-25T11:52:41.731920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:41.853451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
atmosphérique 168
82.0%
turbo 37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t 205
 
8.7%
o 205
 
8.7%
r 205
 
8.7%
u 205
 
8.7%
a 168
 
7.1%
m 168
 
7.1%
s 168
 
7.1%
p 168
 
7.1%
h 168
 
7.1%
é 168
 
7.1%
Other values (4) 541
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2369
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 205
 
8.7%
o 205
 
8.7%
r 205
 
8.7%
u 205
 
8.7%
a 168
 
7.1%
m 168
 
7.1%
s 168
 
7.1%
p 168
 
7.1%
h 168
 
7.1%
é 168
 
7.1%
Other values (4) 541
22.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2369
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 205
 
8.7%
o 205
 
8.7%
r 205
 
8.7%
u 205
 
8.7%
a 168
 
7.1%
m 168
 
7.1%
s 168
 
7.1%
p 168
 
7.1%
h 168
 
7.1%
é 168
 
7.1%
Other values (4) 541
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2201
92.9%
None 168
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 205
9.3%
o 205
9.3%
r 205
9.3%
u 205
9.3%
a 168
7.6%
m 168
7.6%
s 168
7.6%
p 168
7.6%
h 168
7.6%
i 168
7.6%
Other values (3) 373
16.9%
None
ValueCountFrequency (%)
é 168
100.0%

nombre_portes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
quatre
115 
deux
90 

Length

Max length6
Median length6
Mean length5.1219512
Min length4

Characters and Unicode

Total characters1050
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdeux
2nd rowdeux
3rd rowdeux
4th rowquatre
5th rowquatre

Common Values

ValueCountFrequency (%)
quatre 115
56.1%
deux 90
43.9%

Length

2023-04-25T11:52:41.953031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:42.080404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
quatre 115
56.1%
deux 90
43.9%

Most occurring characters

ValueCountFrequency (%)
u 205
19.5%
e 205
19.5%
q 115
11.0%
a 115
11.0%
t 115
11.0%
r 115
11.0%
d 90
8.6%
x 90
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1050
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 205
19.5%
e 205
19.5%
q 115
11.0%
a 115
11.0%
t 115
11.0%
r 115
11.0%
d 90
8.6%
x 90
8.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1050
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 205
19.5%
e 205
19.5%
q 115
11.0%
a 115
11.0%
t 115
11.0%
r 115
11.0%
d 90
8.6%
x 90
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 205
19.5%
e 205
19.5%
q 115
11.0%
a 115
11.0%
t 115
11.0%
r 115
11.0%
d 90
8.6%
x 90
8.6%

type_vehicule
Categorical

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
berline
96 
berline compacte
70 
break
25 
hardtop
 
8
convertible
 
6

Length

Max length16
Median length7
Mean length9.9463415
Min length5

Characters and Unicode

Total characters2039
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowberline compacte
4th rowberline
5th rowberline

Common Values

ValueCountFrequency (%)
berline 96
46.8%
berline compacte 70
34.1%
break 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Length

2023-04-25T11:52:42.174726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:42.297525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
berline 166
60.4%
compacte 70
25.5%
break 25
 
9.1%
hardtop 8
 
2.9%
convertible 6
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e 439
21.5%
r 205
10.1%
b 197
9.7%
l 172
 
8.4%
i 172
 
8.4%
n 172
 
8.4%
c 146
 
7.2%
a 103
 
5.1%
t 84
 
4.1%
o 84
 
4.1%
Other values (7) 265
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1969
96.6%
Space Separator 70
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 439
22.3%
r 205
10.4%
b 197
10.0%
l 172
 
8.7%
i 172
 
8.7%
n 172
 
8.7%
c 146
 
7.4%
a 103
 
5.2%
t 84
 
4.3%
o 84
 
4.3%
Other values (6) 195
9.9%
Space Separator
ValueCountFrequency (%)
70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1969
96.6%
Common 70
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 439
22.3%
r 205
10.4%
b 197
10.0%
l 172
 
8.7%
i 172
 
8.7%
n 172
 
8.7%
c 146
 
7.4%
a 103
 
5.2%
t 84
 
4.3%
o 84
 
4.3%
Other values (6) 195
9.9%
Common
ValueCountFrequency (%)
70
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 439
21.5%
r 205
10.1%
b 197
9.7%
l 172
 
8.4%
i 172
 
8.4%
n 172
 
8.4%
c 146
 
7.2%
a 103
 
5.1%
t 84
 
4.1%
o 84
 
4.1%
Other values (7) 265
13.0%

roues_motrices
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
traction
120 
propulsion
76 
quatre roues motrices
 
9

Length

Max length21
Median length8
Mean length9.3121951
Min length8

Characters and Unicode

Total characters1909
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpropulsion
2nd rowpropulsion
3rd rowpropulsion
4th rowtraction
5th rowquatre roues motrices

Common Values

ValueCountFrequency (%)
traction 120
58.5%
propulsion 76
37.1%
quatre roues motrices 9
 
4.4%

Length

2023-04-25T11:52:42.410884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:42.529148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
traction 120
53.8%
propulsion 76
34.1%
quatre 9
 
4.0%
roues 9
 
4.0%
motrices 9
 
4.0%

Most occurring characters

ValueCountFrequency (%)
o 290
15.2%
t 258
13.5%
r 223
11.7%
i 205
10.7%
n 196
10.3%
p 152
8.0%
a 129
6.8%
c 129
6.8%
u 94
 
4.9%
s 94
 
4.9%
Other values (5) 139
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1891
99.1%
Space Separator 18
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 290
15.3%
t 258
13.6%
r 223
11.8%
i 205
10.8%
n 196
10.4%
p 152
8.0%
a 129
6.8%
c 129
6.8%
u 94
 
5.0%
s 94
 
5.0%
Other values (4) 121
6.4%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1891
99.1%
Common 18
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 290
15.3%
t 258
13.6%
r 223
11.8%
i 205
10.8%
n 196
10.4%
p 152
8.0%
a 129
6.8%
c 129
6.8%
u 94
 
5.0%
s 94
 
5.0%
Other values (4) 121
6.4%
Common
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 290
15.2%
t 258
13.5%
r 223
11.7%
i 205
10.7%
n 196
10.3%
p 152
8.0%
a 129
6.8%
c 129
6.8%
u 94
 
4.9%
s 94
 
4.9%
Other values (5) 139
7.3%

emplacement_moteur
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
avant
202 
arrière
 
3

Length

Max length7
Median length5
Mean length5.0292683
Min length5

Characters and Unicode

Total characters1031
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowavant
2nd rowavant
3rd rowavant
4th rowavant
5th rowavant

Common Values

ValueCountFrequency (%)
avant 202
98.5%
arrière 3
 
1.5%

Length

2023-04-25T11:52:42.632379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:42.750365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
avant 202
98.5%
arrière 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 407
39.5%
v 202
19.6%
n 202
19.6%
t 202
19.6%
r 9
 
0.9%
i 3
 
0.3%
è 3
 
0.3%
e 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1031
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 407
39.5%
v 202
19.6%
n 202
19.6%
t 202
19.6%
r 9
 
0.9%
i 3
 
0.3%
è 3
 
0.3%
e 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1031
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 407
39.5%
v 202
19.6%
n 202
19.6%
t 202
19.6%
r 9
 
0.9%
i 3
 
0.3%
è 3
 
0.3%
e 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1028
99.7%
None 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 407
39.6%
v 202
19.6%
n 202
19.6%
t 202
19.6%
r 9
 
0.9%
i 3
 
0.3%
e 3
 
0.3%
None
ValueCountFrequency (%)
è 3
100.0%

empattement
Real number (ℝ)

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:42.864518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2023-04-25T11:52:42.995806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.2%
93.7 20
 
9.8%
95.7 13
 
6.3%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
104.3 6
 
2.9%
100.4 6
 
2.9%
107.9 6
 
2.9%
98.8 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.4%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.4%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

longueur
Real number (ℝ)

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:43.136318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2023-04-25T11:52:43.281585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.3%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
186.6 6
 
2.9%
172 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.3%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

largeur
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:43.436859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2023-04-25T11:52:43.587471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.7%
66.5 23
 
11.2%
65.4 15
 
7.3%
63.6 11
 
5.4%
64.4 10
 
4.9%
68.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.7%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

hauteur
Real number (ℝ)

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:43.723002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2023-04-25T11:52:43.864276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.8%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.4%
50.8 14
6.8%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

poids_vehicule
Real number (ℝ)

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:44.003360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2023-04-25T11:52:44.137314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

type_moteur
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-04-25T11:52:44.258898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:44.394176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 641
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 641
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

nombre_cylindres
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-04-25T11:52:44.505654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:44.636976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 800
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

taille_moteur
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:44.760696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2023-04-25T11:52:45.135415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.3%
92 15
 
7.3%
97 14
 
6.8%
98 14
 
6.8%
108 13
 
6.3%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.4%
92 15
7.3%
97 14
6.8%
98 14
6.8%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-04-25T11:52:45.253879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:52:45.388953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 719
90.0%
Decimal Number 80
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 719
90.0%
Common 80
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

taux_alésage
Real number (ℝ)

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3297561
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:45.517992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27084371
Coefficient of variation (CV)0.081340404
Kurtosis-0.78504183
Mean3.3297561
Median Absolute Deviation (MAD)0.26
Skewness0.020156418
Sum682.6
Variance0.073356313
MonotonicityNot monotonic
2023-04-25T11:52:45.639098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.43 8
 
3.9%
3.78 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 83
40.5%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.4%
3.03 12
5.9%
3.05 6
2.9%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.4%
3.63 2
 
1.0%
3.62 23
11.2%
3.61 1
 
0.5%
3.6 1
 
0.5%

course
Real number (ℝ)

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2554146
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:45.769547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31359701
Coefficient of variation (CV)0.096330898
Kurtosis2.1743964
Mean3.2554146
Median Absolute Deviation (MAD)0.14
Skewness-0.68970458
Sum667.36
Variance0.098343087
MonotonicityNot monotonic
2023-04-25T11:52:45.883156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.4 20
 
9.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.11 6
 
2.9%
Other values (27) 87
42.4%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.4%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.8%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.4%
3.58 6
2.9%
3.54 4
2.0%
3.52 5
2.4%
3.5 6
2.9%
3.47 4
2.0%
3.46 8
3.9%

taux_compression
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:45.994684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2023-04-25T11:52:46.098359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

chevaux
Real number (ℝ)

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.11707
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:46.227431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.544167
Coefficient of variation (CV)0.37980483
Kurtosis2.6840062
Mean104.11707
Median Absolute Deviation (MAD)25
Skewness1.4053102
Sum21344
Variance1563.7411
MonotonicityNot monotonic
2023-04-25T11:52:46.356657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 117
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

tour_moteur
Real number (ℝ)

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.122
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:46.462276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.98564
Coefficient of variation (CV)0.093068155
Kurtosis0.086755856
Mean5125.122
Median Absolute Deviation (MAD)300
Skewness0.075158722
Sum1050650
Variance227515.3
MonotonicityNot monotonic
2023-04-25T11:52:46.562948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.4%
Other values (13) 34
16.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

consommation_ville
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:46.673383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2023-04-25T11:52:46.800039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.7%
27 14
 
6.8%
17 13
 
6.3%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

consommation_autoroute
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:46.939389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2023-04-25T11:52:47.059861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.8%
37 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

prix
Real number (ℝ)

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.711
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:52:47.195877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.8523
Coefficient of variation (CV)0.60171925
Kurtosis3.0516479
Mean13276.711
Median Absolute Deviation (MAD)3306
Skewness1.7776782
Sum2721725.7
Variance63821762
MonotonicityNot monotonic
2023-04-25T11:52:47.326063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
9279 2
 
1.0%
7898 2
 
1.0%
8916.5 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (179) 185
90.2%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

marque
Categorical

Distinct28
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
31 
nissan
17 
mazda
15 
honda
13 
mitsubishi
13 
Other values (23)
116 

Length

Max length11
Median length10
Mean length6.1463415
Min length2

Characters and Unicode

Total characters1260
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.4%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 31
15.1%
nissan 17
 
8.3%
mazda 15
 
7.3%
honda 13
 
6.3%
mitsubishi 13
 
6.3%
subaru 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
volkswagen 9
 
4.4%
dodge 9
 
4.4%
Other values (18) 64
31.2%

Length

2023-04-25T11:52:47.454053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 31
15.1%
nissan 18
 
8.8%
mazda 15
 
7.3%
honda 13
 
6.3%
mitsubishi 13
 
6.3%
subaru 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
volkswagen 9
 
4.4%
dodge 9
 
4.4%
Other values (17) 63
30.7%

Most occurring characters

ValueCountFrequency (%)
a 152
12.1%
o 150
 
11.9%
t 100
 
7.9%
s 99
 
7.9%
u 85
 
6.7%
i 76
 
6.0%
n 60
 
4.8%
e 58
 
4.6%
d 55
 
4.4%
m 49
 
3.9%
Other values (17) 376
29.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1256
99.7%
Dash Punctuation 3
 
0.2%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 152
12.1%
o 150
11.9%
t 100
 
8.0%
s 99
 
7.9%
u 85
 
6.8%
i 76
 
6.1%
n 60
 
4.8%
e 58
 
4.6%
d 55
 
4.4%
m 49
 
3.9%
Other values (15) 372
29.6%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1257
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 152
12.1%
o 150
11.9%
t 100
 
8.0%
s 99
 
7.9%
u 85
 
6.8%
i 76
 
6.0%
n 60
 
4.8%
e 58
 
4.6%
d 55
 
4.4%
m 49
 
3.9%
Other values (16) 373
29.7%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 152
12.1%
o 150
 
11.9%
t 100
 
7.9%
s 99
 
7.9%
u 85
 
6.7%
i 76
 
6.0%
n 60
 
4.8%
e 58
 
4.6%
d 55
 
4.4%
m 49
 
3.9%
Other values (17) 376
29.8%

modèle
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct141
Distinct (%)69.5%
Missing2
Missing (%)1.0%
Memory size1.7 KiB
504
 
6
corolla
 
6
corona
 
6
dl
 
4
civic
 
3
Other values (136)
178 

Length

Max length25
Median length18
Mean length7.0788177
Min length2

Characters and Unicode

Total characters1437
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)50.2%

Sample

1st rowgiulia
2nd rowstelvio
3rd rowQuadrifoglio
4th row100 ls
5th row100ls

Common Values

ValueCountFrequency (%)
504 6
 
2.9%
corolla 6
 
2.9%
corona 6
 
2.9%
dl 4
 
2.0%
civic 3
 
1.5%
mark ii 3
 
1.5%
g4 3
 
1.5%
rabbit 3
 
1.5%
outlander 3
 
1.5%
mirage g4 3
 
1.5%
Other values (131) 163
79.5%

Length

2023-04-25T11:52:47.564653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
corolla 12
 
4.2%
sw 10
 
3.5%
corona 9
 
3.2%
glc 8
 
2.8%
civic 8
 
2.8%
custom 8
 
2.8%
504 7
 
2.5%
g4 6
 
2.1%
deluxe 5
 
1.8%
mirage 4
 
1.4%
Other values (141) 206
72.8%

Most occurring characters

ValueCountFrequency (%)
c 108
 
7.5%
a 107
 
7.4%
l 103
 
7.2%
r 100
 
7.0%
e 100
 
7.0%
o 93
 
6.5%
82
 
5.7%
i 71
 
4.9%
t 67
 
4.7%
s 54
 
3.8%
Other values (35) 552
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1128
78.5%
Decimal Number 179
 
12.5%
Space Separator 82
 
5.7%
Open Punctuation 13
 
0.9%
Close Punctuation 13
 
0.9%
Uppercase Letter 12
 
0.8%
Dash Punctuation 10
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 108
 
9.6%
a 107
 
9.5%
l 103
 
9.1%
r 100
 
8.9%
e 100
 
8.9%
o 93
 
8.2%
i 71
 
6.3%
t 67
 
5.9%
s 54
 
4.8%
u 41
 
3.6%
Other values (15) 284
25.2%
Decimal Number
ValueCountFrequency (%)
0 44
24.6%
4 37
20.7%
1 23
12.8%
2 21
11.7%
5 18
10.1%
9 12
 
6.7%
6 12
 
6.7%
3 10
 
5.6%
7 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M 4
33.3%
D 3
25.0%
Q 1
 
8.3%
U 1
 
8.3%
X 1
 
8.3%
V 1
 
8.3%
C 1
 
8.3%
Space Separator
ValueCountFrequency (%)
82
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140
79.3%
Common 297
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 108
 
9.5%
a 107
 
9.4%
l 103
 
9.0%
r 100
 
8.8%
e 100
 
8.8%
o 93
 
8.2%
i 71
 
6.2%
t 67
 
5.9%
s 54
 
4.7%
u 41
 
3.6%
Other values (22) 296
26.0%
Common
ValueCountFrequency (%)
82
27.6%
0 44
14.8%
4 37
12.5%
1 23
 
7.7%
2 21
 
7.1%
5 18
 
6.1%
( 13
 
4.4%
) 13
 
4.4%
9 12
 
4.0%
6 12
 
4.0%
Other values (3) 22
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 108
 
7.5%
a 107
 
7.4%
l 103
 
7.2%
r 100
 
7.0%
e 100
 
7.0%
o 93
 
6.5%
82
 
5.7%
i 71
 
4.9%
t 67
 
4.7%
s 54
 
3.8%
Other values (35) 552
38.4%

Interactions

2023-04-25T11:52:38.619380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:13.031156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:14.850400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.436884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:18.116755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:19.782954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:21.890577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.448819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:25.067737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:26.586934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.193073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:29.907129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.479583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:33.021436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:34.811480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:36.688850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:38.728480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:13.152967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:14.967928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.544073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:18.240363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:19.889683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:21.996310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.556147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:25.168972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:26.695707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.288422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:30.014506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.575954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:33.145756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:34.946525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:36.804008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:38.828359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:13.262893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:15.060936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.640458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:18.345005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:19.992022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:22.088293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.661124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:25.257424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:26.789615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.372144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:30.107842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.661808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:33.258393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:35.062205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:36.904627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:38.936169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:13.386036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:15.168166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.748116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:18.451777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:20.124813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:22.186200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.777347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:25.358192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:26.896469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.468127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:30.208095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.754788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:33.378890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:35.180668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:37.008547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:39.044762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:13.513653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:15.269849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.860829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:18.563665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:20.253553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:22.283366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.891944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:25.457967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:27.002362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.564291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:30.312005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.851499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:33.494111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:35.300567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:37.123609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:39.146721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:13.630884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:15.370151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.967659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:18.666896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:20.376631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:22.376567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:24.000426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:25.551797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:27.101221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.656151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:30.411245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.941104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:33.598207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:35.415648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:37.230053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:39.247364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:13.774528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:15.468386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:17.065707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:18.772201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:20.475309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:22.474907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:24.103622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:25.648668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:27.196726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.747802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:30.508235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:32.033794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:33.706218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:35.527655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:37.339794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:39.346031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:13.897447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:15.561271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:17.162087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:18.871682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:20.570813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:22.571079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:24.197808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:25.737066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:27.298823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.838269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:30.602661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:32.121669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:33.822358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T11:52:37.442126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T11:52:15.656299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:17.257383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-25T11:52:21.448579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.046031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:24.678207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:26.199468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:27.782889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:29.293786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.081357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:32.568278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:34.369867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:36.255296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:37.959922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:39.946091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:14.517528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.126968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:17.779267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:19.461881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:21.558931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.134869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:24.765379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:26.285800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:27.875537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:29.614130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.170402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:32.654138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:34.467392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:36.363577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:38.053255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:40.061585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:14.624695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.227449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:17.886266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:19.567892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:21.676065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.236639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:24.863631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:26.382601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:27.980635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:29.707179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.268855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:32.751602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:34.577654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:36.473342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:38.390219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:40.187829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:14.734769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:16.330484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:17.999278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:19.677431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:21.781867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:23.343456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:24.966021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:26.484809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:28.087903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:29.807063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:31.373202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:32.883325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:34.697669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:36.581445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:52:38.508852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-25T11:52:47.688494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
identifiantetat_de_routeempattementlongueurlargeurhauteurpoids_vehiculetaille_moteurtaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixcarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurtype_moteurnombre_cylindressysteme_carburantmarque
identifiant1.000-0.1570.1970.1550.1490.2630.1240.0890.273-0.1600.1510.005-0.2300.0560.0210.0200.2890.2610.3430.1770.4240.3060.4120.2790.3850.795
etat_de_route-0.1571.000-0.538-0.396-0.254-0.523-0.256-0.177-0.170-0.0190.023-0.0100.282-0.0180.053-0.1450.2170.1850.6840.3340.2660.2720.2220.1600.2660.445
empattement0.197-0.5381.0000.9120.8120.6330.7650.6480.5370.227-0.1260.505-0.312-0.493-0.5390.6820.3410.3100.4450.3340.4170.5680.3530.3160.2260.510
longueur0.155-0.3960.9121.0000.8880.5250.8900.7830.6390.187-0.1930.661-0.269-0.670-0.6980.8040.1100.2070.3650.2410.4090.0000.3170.3560.3260.490
largeur0.149-0.2540.8120.8881.0000.3500.8640.7710.6100.240-0.1460.689-0.199-0.688-0.7010.8110.2330.3010.3050.1280.4030.1600.3690.5670.2460.515
hauteur0.263-0.5230.6330.5250.3501.0000.3460.2000.216-0.0180.0000.011-0.296-0.069-0.1330.2430.2770.2370.5410.4970.3600.2720.3880.3500.2920.461
poids_vehicule0.124-0.2560.7650.8900.8640.3461.0000.8780.7020.163-0.2190.808-0.236-0.813-0.8340.9090.3050.3750.2740.2300.4560.1000.3270.4820.2920.488
taille_moteur0.089-0.1770.6480.7830.7710.2000.8781.0000.7010.292-0.2350.817-0.273-0.730-0.7210.8260.1570.2710.2070.2020.4690.6190.5270.6420.3330.515
taux_alésage0.273-0.1700.5370.6390.6100.2160.7020.7011.000-0.083-0.1600.639-0.298-0.609-0.6150.6440.1680.3350.1630.1510.4340.3270.4180.2580.3450.531
course-0.160-0.0190.2270.1870.240-0.0180.1630.292-0.0831.000-0.0700.130-0.074-0.030-0.0300.1110.3750.2650.1320.1510.3380.6150.4040.2390.3030.560
taux_compression0.1510.023-0.126-0.193-0.1460.000-0.219-0.235-0.160-0.0701.000-0.353-0.0220.4790.445-0.1740.9930.5540.1860.0480.1140.0000.3380.5210.5180.485
chevaux0.005-0.0100.5050.6610.6890.0110.8080.8170.6390.130-0.3531.0000.113-0.911-0.8860.8550.2190.3430.1710.1890.4020.8430.5140.5640.3170.453
tour_moteur-0.2300.282-0.312-0.269-0.199-0.296-0.236-0.273-0.298-0.074-0.0220.1131.000-0.131-0.057-0.0660.5940.3110.2440.0740.2420.4480.3590.2830.3630.460
consommation_ville0.056-0.018-0.493-0.670-0.688-0.069-0.813-0.730-0.609-0.0300.479-0.911-0.1311.0000.968-0.8290.3890.1860.0030.0000.3800.1100.2090.4240.3040.341
consommation_autoroute0.0210.053-0.539-0.698-0.701-0.133-0.834-0.721-0.615-0.0300.445-0.886-0.0570.9681.000-0.8230.3360.3190.1190.0000.4370.1010.3250.5000.3410.401
prix0.020-0.1450.6820.8040.8110.2430.9090.8260.6440.111-0.1740.855-0.066-0.829-0.8231.0000.3380.4070.0000.2290.4510.4510.2880.4290.2900.373
carburant0.2890.2170.3410.1100.2330.2770.3050.1570.1680.3750.9930.2190.5940.3890.3360.3381.0000.3740.1610.1730.0880.0000.2500.1550.9850.383
turbo0.2610.1850.3100.2070.3010.2370.3750.2710.3350.2650.5540.3430.3110.1860.3190.4070.3741.0000.0000.0000.1180.0000.1500.1960.6100.378
nombre_portes0.3430.6840.4450.3650.3050.5410.2740.2070.1630.1320.1860.1710.2440.0030.1190.0000.1610.0001.0000.7410.0500.0670.2000.1340.2450.338
type_vehicule0.1770.3340.3340.2410.1280.4970.2300.2020.1510.1510.0480.1890.0740.0000.0000.2290.1730.0000.7411.0000.2140.4380.1320.0680.1440.352
roues_motrices0.4240.2660.4170.4090.4030.3600.4560.4690.4340.3380.1140.4020.2420.3800.4370.4510.0880.1180.0500.2141.0000.1240.4250.3360.3870.583
emplacement_moteur0.3060.2720.5680.0000.1600.2720.1000.6190.3270.6150.0000.8430.4480.1100.1010.4510.0000.0000.0670.4380.1241.0000.3990.2880.0000.729
type_moteur0.4120.2220.3530.3170.3690.3880.3270.5270.4180.4040.3380.5140.3590.2090.3250.2880.2500.1500.2000.1320.4250.3991.0000.5460.3770.627
nombre_cylindres0.2790.1600.3160.3560.5670.3500.4820.6420.2580.2390.5210.5640.2830.4240.5000.4290.1550.1960.1340.0680.3360.2880.5461.0000.3730.531
systeme_carburant0.3850.2660.2260.3260.2460.2920.2920.3330.3450.3030.5180.3170.3630.3040.3410.2900.9850.6100.2450.1440.3870.0000.3770.3731.0000.492
marque0.7950.4450.5100.4900.5150.4610.4880.5150.5310.5600.4850.4530.4600.3410.4010.3730.3830.3780.3380.3520.5830.7290.6270.5310.4921.000

Missing values

2023-04-25T11:52:40.407188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-25T11:52:40.836174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

identifiantetat_de_routecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueurlargeurhauteurpoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarquemodèle
013essenceatmosphériquedeuxconvertiblepropulsionavant88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495.000alfa-romerogiulia
123essenceatmosphériquedeuxconvertiblepropulsionavant88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500.000alfa-romerostelvio
231essenceatmosphériquedeuxberline compactepropulsionavant94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500.000alfa-romeroQuadrifoglio
342essenceatmosphériquequatreberlinetractionavant99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950.000audi100 ls
452essenceatmosphériquequatreberlinequatre roues motricesavant99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450.000audi100ls
562essenceatmosphériquedeuxberlinetractionavant99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250.000audifox
671essenceatmosphériquequatreberlinetractionavant105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710.000audi100ls
781essenceatmosphériquequatrebreaktractionavant105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920.000audi5000
891essenceturboquatreberlinetractionavant105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875.000audi4000
9100essenceturbodeuxberline compactequatre roues motricesavant99.5178.267.952.03053ohcfive131mpfi3.133.407.01605500162217859.167audi5000s (diesel)
identifiantetat_de_routecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueurlargeurhauteurpoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixmarquemodèle
195196-1essenceatmosphériquequatrebreakpropulsionavant104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415.0volvo144ea
196197-2essenceatmosphériquequatreberlinepropulsionavant104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985.0volvo244dl
197198-1essenceatmosphériquequatrebreakpropulsionavant104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515.0volvo245
198199-2essenceturboquatreberlinepropulsionavant104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420.0volvo264gl
199200-1essenceturboquatrebreakpropulsionavant104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950.0volvodiesel
200201-1essenceatmosphériquequatreberlinepropulsionavant109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845.0volvo145e (sw)
201202-1essenceturboquatreberlinepropulsionavant109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045.0volvo144ea
202203-1essenceatmosphériquequatreberlinepropulsionavant109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485.0volvo244dl
203204-1dieselturboquatreberlinepropulsionavant109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470.0volvo246
204205-1essenceturboquatreberlinepropulsionavant109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625.0volvo264gl